began work on v3
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rpotter6298
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from .base import DatasetProfile, SimpleDatasetProfile, SlotDescriptor
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from .papila import PapilaProfile, build_papila_profile
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__all__ = [
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"DatasetProfile",
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"SimpleDatasetProfile",
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"SlotDescriptor",
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"PapilaProfile",
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"build_papila_profile",
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]
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Protocol, Any
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import pandas as pd
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@dataclass(frozen=True)
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class SlotDescriptor:
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"""
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Metadata for a generic batch slot key (e.g., image_1, matrix_1).
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"""
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key: str
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kind: str
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description: str
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required: bool = True
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shape_hint: str | None = None
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class DatasetProfile(Protocol):
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"""
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Dataset-specific wiring that stays outside the generic V2 engine.
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"""
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name: str
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patient_col: str
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label_col: str
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def slot_descriptors(self) -> dict[str, SlotDescriptor]:
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...
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def semantic_aliases(self) -> dict[str, str]:
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...
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def build_samples(self, *, df: pd.DataFrame, clinical: Any) -> list[dict[str, Any]]:
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...
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@dataclass(frozen=True)
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class SimpleDatasetProfile:
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name: str
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patient_col: str
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label_col: str
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slots: dict[str, SlotDescriptor]
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aliases: dict[str, str]
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def slot_descriptors(self) -> dict[str, SlotDescriptor]:
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return dict(self.slots)
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def semantic_aliases(self) -> dict[str, str]:
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return dict(self.aliases)
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@@ -0,0 +1,155 @@
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from __future__ import annotations
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import pandas as pd
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from dataclasses import dataclass
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from .base import SimpleDatasetProfile, SlotDescriptor
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@dataclass(frozen=True)
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class PapilaProfile(SimpleDatasetProfile):
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sample_mode: str = "patient" # "patient" | "eye"
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def build_samples(self, *, df: pd.DataFrame, clinical) -> list[dict[str, object]]:
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samples: list[dict[str, object]] = []
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patient_col = self.patient_col
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label_col = self.label_col
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mode = (self.sample_mode or "patient").lower()
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if mode not in {"patient", "eye"}:
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raise ValueError(f"Unsupported sample_mode '{self.sample_mode}'. Expected 'patient' or 'eye'.")
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if mode == "eye":
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for _, row in df.iterrows():
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pid = row[patient_col]
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label = row[label_col]
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image_1 = clinical.get_image_path(row) if hasattr(clinical, "get_image_path") else None
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matrix_1 = clinical.vectorize_row(row) if hasattr(clinical, "vectorize_row") else None
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samples.append(
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{
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"id_1": pid,
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"label_1": label,
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"image_1": image_1,
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"matrix_1": matrix_1,
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}
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)
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return samples
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for pid, grp in df.groupby(patient_col):
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label_series = grp[label_col]
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if label_series.empty:
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continue
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mode_vals = label_series.mode()
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label = mode_vals.iloc[0] if not mode_vals.empty else label_series.iloc[0]
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def _row_for_eye(eye: str):
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if "eyeID" not in grp.columns:
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return None
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match = grp[grp["eyeID"].astype(str).str.upper() == eye]
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if match.empty:
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return None
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return match.iloc[0]
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row_od = _row_for_eye("OD")
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row_os = _row_for_eye("OS")
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row_any = grp.iloc[0]
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image_1 = clinical.get_image_path(row_od) if row_od is not None else None
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image_2 = clinical.get_image_path(row_os) if row_os is not None else None
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matrix_1 = clinical.vectorize_row(row_od) if row_od is not None else None
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matrix_2 = clinical.vectorize_row(row_os) if row_os is not None else None
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if image_1 is None and hasattr(clinical, "get_image_path"):
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image_1 = clinical.get_image_path(row_any)
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if matrix_1 is None and hasattr(clinical, "vectorize_row"):
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matrix_1 = clinical.vectorize_row(row_any)
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samples.append(
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{
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"id_1": pid,
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"label_1": label,
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"image_1": image_1,
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"image_2": image_2,
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"matrix_1": matrix_1,
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"matrix_2": matrix_2,
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}
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)
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return samples
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def build_papila_profile(
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*,
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patient_col: str = "Patient ID",
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label_col: str = "Diagnosis",
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sample_mode: str = "patient",
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) -> PapilaProfile:
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"""
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PAPILA-specific semantic map for generic V2 slot keys.
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The engine remains slot-based (image_1/image_2/matrix_1/...).
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PAPILA meaning is captured here so run config stays dataset-local.
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"""
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slots = {
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"id_1": SlotDescriptor(
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key="id_1",
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kind="id",
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description=f"Patient identifier column ({patient_col})",
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required=True,
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shape_hint="scalar",
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),
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"label_1": SlotDescriptor(
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key="label_1",
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kind="label",
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description=f"Diagnosis label column ({label_col})",
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required=True,
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shape_hint="scalar",
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),
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"image_1": SlotDescriptor(
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key="image_1",
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kind="image",
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description="Fundus image slot 1 (PAPILA: OD / right eye)",
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required=False,
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shape_hint="HWC or CHW",
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),
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"image_2": SlotDescriptor(
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key="image_2",
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kind="image",
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description="Fundus image slot 2 (PAPILA: OS / left eye)",
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required=False,
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shape_hint="HWC or CHW",
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),
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"matrix_1": SlotDescriptor(
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key="matrix_1",
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kind="matrix",
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description="Clinical metadata feature vector",
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required=False,
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shape_hint="[feature_dim]",
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),
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"matrix_2": SlotDescriptor(
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key="matrix_2",
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kind="matrix",
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description="Optional auxiliary tabular vector (reserved for experiments)",
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required=False,
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shape_hint="[feature_dim_2]",
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),
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}
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aliases = {
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"id_1": "patient_id",
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"label_1": "diagnosis",
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"image_1": "od_fundus",
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"image_2": "os_fundus",
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"matrix_1": "clinical_metadata",
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"matrix_2": "aux_metadata",
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}
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return PapilaProfile(
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name="papila",
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patient_col=patient_col,
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label_col=label_col,
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slots=slots,
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aliases=aliases,
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sample_mode=sample_mode,
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)
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